门控自进化大语言模型智能体的动力学理论
A Kinetic Theory of the Gated Self-Evolving LLM Agent
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中文总结 AI 辅助
本文提出门控自进化LLM智能体的动力学理论，将插件实例视为硬球，构建严格层、动力学层和测量层，预测并验证密度门控的群体涨落缩放。
中文摘要 AI 辅助
我们在LLM智能体的自进化过程中发现了流体动力学的痕迹，并给出了预测这些痕迹的动力学理论。门控自进化是指智能体在验证门控下重写自身技能的循环。自进化研究一直将智能体视为一个整体单元；我们则研究其内部的个体实例。这里的智能体基于DSH插件：它在DeepSeek Harness（DSH）上生产运行，其插件满足四个架构属性（排列对称性、可逆性、无环性、类型化契约），这允许将这些实例视为相同的硬球；因此该理论适用于DSH类插件群体。在此范围内，论文构建了三个理论层。严格层独立于任何类比，包括一个任意时间命中时间证书，用于界定达到任何规定改进的期望轮数；一个为保留的验证预算定价的解析定律；以及一个分离定理：守护进程必须保持在群体之外，因为将评估器与评估对象合并会使证书失效。动力学层是在插件×版本×任务网格上的主方程，包含四个算子（碰撞、反应、外部场、门控），其中碰撞即共激活。其矩层次（将气体转化为流体方程的一步）产生了可证伪的统计特征。整个过程中，流体解读是一种有界类比：动量不守恒，因此不存在纳维-斯托克斯极限。测量层运行在一个忠实的最小实例上，即在一个单模型、单任务族的WebShop环境中，使用共激活探针每集检索一个的大型四参数技能插件库；每个元素通过架构角色映射到DSH循环。群体涨落缩放是密度门控的：在稀疏编辑密度下不可见，在三倍密度下以预测速率出现，作为方向性证据。
英文摘要
We find traces of fluid dynamics in the self-evolution of an LLM agent, and give the kinetic theory that predicts them. Gated self-evolution is the loop in which an agent rewrites its own skills under a validation gate. Self-evolution research has treated the agent as the unit; we study instead the individual instances inside it. Here the agent is DSH-plugin-based: it runs in production on DeepSeek Harness (DSH), and its plugins satisfy four architectural properties (permutation symmetry, reversibility, acyclicity, typed contracts), which license treating these instances as identical hard spheres; the theory is accordingly scoped to DSH-class plugin populations. On this scope the paper builds three theory layers. The rigorous layer, independent of any analogy, comprises an any-time hitting-time certificate bounding the expected rounds to any prescribed improvement, a resolution law that prices held-out validation budgets, and a separation theorem: the daemon must stay outside the population, because merging evaluator with evaluated voids the certificate. The kinetic layer is a master equation over the plugin x version x task grid with four operators (collision, reaction, external field, gate), where collision is co-activation. Its moment hierarchy, the step that turns a gas into fluid equations, generates the falsifiable statistical signatures. Throughout, the fluid reading is a bounded analogy: momentum is not conserved, so no Navier-Stokes limit exists. The measured layer runs on a faithful minimal instance, a large library of four-parameter skill plugins retrieved one per episode with a co-activation probe, in a one-model, one-task-family WebShop environment; every element maps to the DSH loop by architectural role. Population fluctuation scaling is density-gated: invisible at sparse edit density, it emerges at the predicted rate under tripled density, as directional evidence.
发表机构
- Neusoft Corporation（东软集团）
机构由 AI 辅助整理，请以论文原文为准。
